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rspace-os

RSpace MCP Server

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by rspace-os

add_note_to_subsample

Add annotations or observations to a specific research subsample to record experimental notes, handling instructions, or observations.

Instructions

Adds annotations or observations to a specific subsample

Usage: Record experimental notes, observations, or handling instructions Returns: Updated subsample information with new note

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subsample_idYes
noteYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • main.py:831-840 (handler)
    The MCP tool handler and registration for 'add_note_to_subsample'. This function implements the core logic by calling the RSpace inventory client to add a note to the specified subsample.
    @mcp.tool(tags={"rspace", "inventory", "samples"})
    def add_note_to_subsample(subsample_id: Union[int, str], note: str) -> dict:
        """
        Adds annotations or observations to a specific subsample
        
        Usage: Record experimental notes, observations, or handling instructions
        Returns: Updated subsample information with new note
        """
        return inv_cli.add_note_to_subsample(subsample_id, note)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool 'Adds annotations or observations' (implying a write/mutation operation) and mentions it 'Returns: Updated subsample information', which gives some behavioral context. However, it lacks critical details like permission requirements, whether notes are editable/deletable, rate limits, or error conditions, leaving significant gaps for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured with a clear purpose statement followed by 'Usage:' and 'Returns:' sections. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a mutation tool with no annotations, 0% schema description coverage, but with an output schema (which handles return values), the description is moderately complete. It covers purpose, usage, and return concept adequately, but lacks behavioral details and parameter semantics that would be needed for full completeness in this context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'subsample_id' and 'note' implicitly through context but doesn't explain their semantics, formats, constraints, or examples. The description adds minimal value beyond what's inferable from parameter names, failing to adequately address the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'adds' and the resource 'annotations or observations to a specific subsample', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'tagDocumentOrNotebookEntry' or 'update_document' which might also involve adding metadata, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Usage:' section provides clear context for when to use this tool ('Record experimental notes, observations, or handling instructions'), which helps guide the agent. However, it doesn't specify when NOT to use it or mention alternatives among the many sibling tools, preventing a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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